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The global consumer goods company Unilever was on pace to hit a number of aggressive targets by 2020 as part of the Unilever Sustainable Living Project, including a goal to halve the waste associated with the disposal of its products. Unilever's Chief Supply Chain Officer Pier Luigi Sigismondi and his team were working towards this goal and had chosen to first focus on three key areas—sugar, tomatoes, and tea—and had analyzed where in the 'farm to fork' value chain product was wasted. This analysis showed that very little was wasted within areas of the value chain directly controlled by Unilever, and most occurred either upstream with its suppliers or downstream with consumers. How could Unilever encourage these actors to change established practices and entrenched behaviors within a short timeframe to help Unilever meet its sustainability targets and also to improve the operations of its partners in the value chain? By encouraging consumers to better manage their food purchases, did Unilever risk harming its own sales or those of its retail customers? Could Unilever encourage industry-wide changes to have a real impact on global environmental sustainability?

This case describes the introduction of a regression analysis model for forecasting guest arrivals to Caesars Palace hotel in Las Vegas, Nevada. The company will use the forecast to staff the front desk in the hotel. The staff is unionized and the company has little flexibility to change staffing levels on a short term basis. The case is set in the context of industry overcapacity and lower customer demand.

The case describes several models that could be used to forecast guest arrivals, including a moving average technique and a multiple regression model. The multiple regression model includes over 40 independent variables, including dummy variables (e.g., to represent day of week, month, year, holidays, paydays) as well as continuous variables to represent customer segment and average daily room rate. The case contains tables showing the output of the regression model, and compares the fit of the moving average and regression models. The case allows students to understand how such a model is developed within an organization and to evaluate the models presented. Students may work with a data file with several years of historical data or they may work with the model description and output results in the case.

Describes how Amazon's distribution system evolved from the company's inception. In 2003, Amazon Europe must decide how to reconfigure its distribution network in light of expected growth, products proliferation, and geographical expansion in Europe. Examines how characteristics of suppliers and customers differ across the markets Amazon serves in Europe. The protagonist must consider the degree of centralization appropriate for the European network, where inventory should be held, what fulfillment models should be used, and how to manage risks of supply disruption.